Detection of breast cancer in infrared images using Discrete Wavelet Transform and Support Vector Machine
Resumo
This work presents a comparative study between two approaches which aim at helping diagnosing patients with breast cancer using thermal images. The first method uses the Discrete Wavelet Transform (DWT) for feature extraction and Support Vector Machine (SVM) to classify the patients. The other method is based on feature extraction with pretrained convolutional neural networks in conjunction with deep neural networks to perform the classification. VGG16, ResNet50 and MobileNet were considered to determine which was best suited to classify patient infrared images. Experimental results showed that DWT in conjunction with SVM presented the best performance on classifying 928 images (489 healthy and 429 sick) with 98% accuracy, 97% sensibility and 98% specificity.
Palavras-chave:
SVM, wavelet, transfer learning, CNN, breast cancer, infrared images
Referências
W. H. Organization, “Breast cancer,” [link] [Accessed: (Jan. 30, 2024)].
I. N. de Câncer, “Estatísticas de câncer,” [link] [Accessed: (Feb. 20, 2024)].
A. Mashekova, Y. Zhao, E. Y. Ng, V. Zarikas, S. C. Fok, and O. Mukhmetov, “Early detection of the breast cancer using infrared technology – a comprehensive review,” Thermal Science and Engineering Progress, vol. 27, p. 101142, 2022. [Online]. Available: [link]
L. F. da Silva, D. C. M. Saade, G. O. Sequeiros, A. C. Silva, A. C. de Paiva, R. de Souza Bravo, and A. Conci, “A new database for breast research with infrared image,” Journal of Medical Imaging and Health Informatics, vol. 4, pp. 92–100, 2014. [Online]. Available: [link]
V. Mishra, Y. Singh, and S. Kumar Rath, “Breast cancer detection from thermograms using feature extraction and machine learning techniques,” in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1–5.
T. Zavvar, I. Al Ridhawi, and A. Abbas, “Thermography-based early-stage breast cancer detection using svm,” in 2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2023, pp. 27–34.
S. Mishra, A. Prakash, S. K. Roy, P. Sharan, and N. Mathur, “Breast cancer detection using thermal images and deep learning,” in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2020, pp. 211–216.
S. Sharma, K. Guleria, S. Tiwari, and S. Kumar, “A deep learning based convolutional neural network model with vgg16 feature extractor for the detection of alzheimer disease using mri scans,” Measurement: Sensors, vol. 24, p. 100506, 2022.
C. B. Gonçalves, J. R. Souza, and H. Fernandes, “Classification of static infrared images using pre-trained cnn for breast cancer detection,” in 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2021, pp. 101–106.
A. Barbhuiya and K. Hemachandran, “Wavelet tranformations & its major applications in digital image processing,” International Journal of Engineering Research & Technology (IJERT), ISSN, vol. 2, no. 3, pp. 1–5, 2013.
S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE transactions on pattern analysis and machine intelligence, vol. 11, no. 7, pp. 674–693, 1989.
A. W. Salehi, S. Khan, G. Gupta, B. I. Alabduallah, A. Almjally, H. Alsolai, T. Siddiqui, and A. Mellit, “A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope,” Sustainability, vol. 15, no. 7, p. 5930, 2023.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
I. N. de Câncer, “Estatísticas de câncer,” [link] [Accessed: (Feb. 20, 2024)].
A. Mashekova, Y. Zhao, E. Y. Ng, V. Zarikas, S. C. Fok, and O. Mukhmetov, “Early detection of the breast cancer using infrared technology – a comprehensive review,” Thermal Science and Engineering Progress, vol. 27, p. 101142, 2022. [Online]. Available: [link]
L. F. da Silva, D. C. M. Saade, G. O. Sequeiros, A. C. Silva, A. C. de Paiva, R. de Souza Bravo, and A. Conci, “A new database for breast research with infrared image,” Journal of Medical Imaging and Health Informatics, vol. 4, pp. 92–100, 2014. [Online]. Available: [link]
V. Mishra, Y. Singh, and S. Kumar Rath, “Breast cancer detection from thermograms using feature extraction and machine learning techniques,” in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1–5.
T. Zavvar, I. Al Ridhawi, and A. Abbas, “Thermography-based early-stage breast cancer detection using svm,” in 2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2023, pp. 27–34.
S. Mishra, A. Prakash, S. K. Roy, P. Sharan, and N. Mathur, “Breast cancer detection using thermal images and deep learning,” in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2020, pp. 211–216.
S. Sharma, K. Guleria, S. Tiwari, and S. Kumar, “A deep learning based convolutional neural network model with vgg16 feature extractor for the detection of alzheimer disease using mri scans,” Measurement: Sensors, vol. 24, p. 100506, 2022.
C. B. Gonçalves, J. R. Souza, and H. Fernandes, “Classification of static infrared images using pre-trained cnn for breast cancer detection,” in 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2021, pp. 101–106.
A. Barbhuiya and K. Hemachandran, “Wavelet tranformations & its major applications in digital image processing,” International Journal of Engineering Research & Technology (IJERT), ISSN, vol. 2, no. 3, pp. 1–5, 2013.
S. G. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” IEEE transactions on pattern analysis and machine intelligence, vol. 11, no. 7, pp. 674–693, 1989.
A. W. Salehi, S. Khan, G. Gupta, B. I. Alabduallah, A. Almjally, H. Alsolai, T. Siddiqui, and A. Mellit, “A study of cnn and transfer learning in medical imaging: Advantages, challenges, future scope,” Sustainability, vol. 15, no. 7, p. 5930, 2023.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
Publicado
06/11/2024
Como Citar
CREPALDI JUNIOR, Antonio Marcio; SOUZA, Pablo Rodrigo de; SILVA NETO, Nelson Luciano da; LEMES, Dimas Augusto Mendes; PICOLO, José; SALES, Guilherme Ribeiro; CORSO, Valentino; BEZERRA, Cides S..
Detection of breast cancer in infrared images using Discrete Wavelet Transform and Support Vector Machine. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 19. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 19-24.
DOI: https://doi.org/10.5753/wvc.2024.34007.
